Instructions to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16
# Run inference directly in the terminal:
llama cli -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16
# Run inference directly in the terminal:
llama cli -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16
# Run inference directly in the terminal:
./llama-cli -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Use Docker

docker model run hf.co/Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

LM Studio
Jan

vLLM

How to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Ollama
How to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with Ollama:
```
ollama run hf.co/Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16
```
Unsloth Desktop

How to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with Docker Model Runner:
```
docker model run hf.co/Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16
```

Lemonade

How to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Run and chat with the model

lemonade run user.qwen3loop-0.6b-sft-deep-supervision-v1-F16

List all available models

lemonade list

Hermes Agent

How to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Lordnyx/qwen3loop-0.6b-sft-deep-supervision-v1:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3Loop-0.6B-SFT-Deep-Supervision (Hotfix 0.2: Suffix Layer Calibration & Unrolled Release)

Qwen3Loop 0.6B is an efficient recursive reasoning model available in two deployment formats:

Native Looped Architecture (28 physical layers executed dynamically 56/42 times via custom engine patch).
Standard Unrolled Architecture (42 physical layers running natively across ALL standard inference tools without any patches).

⚠️ Important Warning & Architecture Selection

🔄 Native Looped vs. Unrolled Architecture Trade-Off:

Native Looped Binaries (modelo_qwen3loop_sft_*.gguf): Maximum VRAM and disk compactness (604 MB in Q8_0, 2.8 GB VRAM). Requires our patched llama.cpp build or Python Qwen3LoopForCausalLM (trust_remote_code=True).

Unrolled Binaries (unrolled_modelo_qwen3loop_sft_*.gguf): 100% Universal Compatibility. This version trades off the weight-sharing VRAM benefit (827 MB in Q8_0, ~3.6 GB VRAM), but in exchange runs natively out-of-the-box in standard vanilla llama.cpp, Ollama, LM Studio, vLLM, and Hugging Face Transformers WITHOUT requiring any C++ patches or custom Python scripts!

🎮 Playground Readiness Notice (Hotfix 0.2)

Following the Hotfix 0.2 update (Suffix Layer Transduction Calibration), both the Native and Unrolled variants have completely eliminated previous formatting collapse, repetition loops, and token dissipation. The model is fully calibrated, highly responsive, and officially USABLE FOR PLAYGROUND and interactive testing!

🎯 Recommended Sampling & Inference Parameters

To achieve optimal generation quality (zero loops, crisp reasoning tags <think>, sharp answers), use the following empirically validated parameters:

Parameter	Recommended Value	Description
Temperature	`0.60` (or `0.0` for code/math)	Balanced creativity and precision
Top-K	`40`	Filters extreme long-tail tokens
Top-P	`0.95`	Nucleus sampling threshold
Repeat Penalty	`1.12`	Prevents loop degradation in small architectures
Repeat Last N	`128`	Repetition penalty lookback buffer
Context Window	`32,768` tokens	Native context length

🔬 Layerwise Probing Evolution (Hotfix 0.2)

Through Layerwise Early-Exit Probing across the 56 virtual execution steps:

Execution Stage / Layer	Before Calibration (Old)	Hotfix 0.2 (Step 75)	Status
Prefix Exit (L06)	29.10%	5.20%	Factual Anchoring
Loop Pass 1 Exit (L20_p1)	35.70%	100.00%	Loop Convergence
Loop Pass 2 Exit (L20_p2) ⭐	67.20%	99.80% 🟢	Peak Recursive Reasoning
Suffix Entry (L21)	44.10%	99.61% 🟢	Smooth Thought Projection
Suffix Mid (L24)	27.10%	96.88% 🟢	Zero Noise
Suffix Final Exit (L27) 🛑	21.90% 🛑	90.62% 🏆	Dissipation Eliminated (+68.7%)

📊 97-Question Full Capability Benchmark

Global Accuracy: 79.38% (77/97) (Net +6.18% gain over DARE-TIES)
Writing & Text Generation: 100.00% (8/8) 🟢
Summarization: 100.00% (7/7) 🟢
Creativity: 100.00% (7/7) 🟢
Robustness: 100.00% (8/8) 🟢
Instruction Following: 90.00% (9/10) 🟢
Deductive Reasoning: 90.00% (9/10) 🟢
General Knowledge: 80.00% (8/10) 🟢
Mathematics & Algebra: 80.00% (8/10) 🟢
Coding & Syntax: 70.00% (7/10) 🟢
Inference Speed: 127.9 - 152.7 tokens/s (NVIDIA RTX 3060 CUDA)

📦 Model Files & Download Options

1. Universal Unrolled Models (No Patches Needed — Recommended for Ollama / LM Studio)

unrolled_modelo_qwen3loop_sft_q8_0.gguf (827 MB): Standard Q8_0 GGUF. Works in vanilla llama.cpp and Ollama.
unrolled_modelo_qwen3loop_sft_f16.gguf (1.63 GB): Standard FP16 GGUF.

2. Compact Native Looped Models (Requires Patched llama.cpp Engine)

modelo_qwen3loop_sft_q8_0.gguf (604 MB): Ultra-compact looped Q8_0 GGUF.
modelo_qwen3loop_sft_f16.gguf (1.19 GB): Ultra-compact looped FP16 GGUF.
model.safetensors / config.json / tokenizer.json: PyTorch / Transformers model.
hotfix0.2.txt: Technical release notes and layerwise probing logs.

Downloads last month: 1,688

Safetensors

Model size

0.6B params

Tensor type

BF16